Gastos Casa
19 August, 2024
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 6/7/2024 | Comida | 67304 | Tami | Supermercado |
| 8/7/2024 | Diosi | 9950 | Tami | Pastitas lata Braloy |
| 10/7/2024 | Comida | 13990 | Tami | Barritas wild soul |
| 12/7/2024 | Parafina | 45533 | Tami | Parafina |
| 12/7/2024 | Gas/Bencina | 31886 | Tami | Bencina Maite |
| 13/7/2024 | Comida | 28083 | Tami | Supermercado |
| 13/7/2024 | Comida | 32210 | Andrés | comida maite e ida farmacia |
| 15/7/2024 | Comida | 11250 | Tami | Minimarket Maite |
| 16/7/2024 | Comida | 4900 | Tami | Torta Maite |
| 16/7/2024 | Comida | 65645 | Andrés | gastos maite (no va lo de mercado pago, q taché en negrita) |
| 18/7/2024 | Comida | 36687 | Tami | Supermercado |
| 18/7/2024 | Comida | 4772 | Andrés | NA |
| 20/7/2024 | VTR | 21990 | Andrés | NA |
| 12/7/2024 | Uber | 2537 | Tami | Vuelta Uber casa delox |
| 25/7/2024 | Comida | 36175 | Andrés | lider pickup |
| 26/7/2024 | Gas | 72850 | Andrés | NA |
| 27/7/2024 | Comida | 4300 | Andrés | biblioteca san camilo |
| 27/7/2024 | Comida | 27300 | Andrés | NA |
| 28/7/2024 | Comida | 67250 | Tami | Supermercado |
| 30/7/2024 | Comida | 30650 | Andrés | piwén |
| 1/8/2024 | Electricidad | 209425 | Andrés | NA |
| 3/8/2024 | Comida | 45130 | Tami | Supermercado |
| 3/8/2024 | Comida | 19490 | Tami | Barritas Wild Soul |
| 11/8/2024 | Comida | 8230 | Tami | Supermercado |
| 8/8/2024 | Comida | 5000 | Andrés | platano y cosas |
| 15/8/2024 | Electricidad | 190084 | Andrés | enel rqlo (dupliqué el gasto para que pagues eso. Yo ya ni uso mi estufa, y tal vez tú deberías usar la mía porque tiene temporizador) |
| 18/8/2024 | VTR | 21990 | Andrés | NA |
| 16/8/2024 | Comida | 11756 | Andrés | lider |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 8.6260e+08 2 7.6474 5e-04 ***
## lag_depvar 1.2403e+11 1 2199.1135 <2e-16 ***
## Residuals 4.2130e+10 747
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 577.6824 13879.99 0.0293078
## 2-0 29682.149 23674.8169 35689.48 0.0000000
## 2-1 22453.311 18950.7910 25955.83 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
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## 259 72207.14 2 70510.86
## 260 67881.00 2 72207.14
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## 266 41348.57 2 45805.29
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## 272 54746.29 2 54407.43
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## 275 69999.29 2 58926.43
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## 279 67969.43 2 61395.43
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## 572 60270.29 2 57894.29
## 573 61011.00 2 60270.29
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## 579 62814.00 2 61092.29
## 580 54908.29 2 62814.00
## 581 62082.00 2 54908.29
## 582 57017.71 2 62082.00
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## 592 44764.14 2 41562.43
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## 596 45870.86 2 53505.00
## 597 52578.00 2 45870.86
## 598 55300.00 2 52578.00
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## 602 53250.43 2 62902.29
## 603 55402.57 2 53250.43
## 604 56291.29 2 55402.57
## 605 58933.57 2 56291.29
## 606 59590.71 2 58933.57
## 607 59065.00 2 59590.71
## 608 52399.57 2 59065.00
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## 610 58262.71 2 60483.43
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## 612 51169.00 2 54939.71
## 613 43113.29 2 51169.00
## 614 56289.71 2 43113.29
## 615 60739.86 2 56289.71
## 616 50363.14 2 60739.86
## 617 62270.86 2 50363.14
## 618 67061.57 2 62270.86
## 619 59609.00 2 67061.57
## 620 85054.00 2 59609.00
## 621 68023.29 2 85054.00
## 622 59242.29 2 68023.29
## 623 61535.14 2 59242.29
## 624 56215.86 2 61535.14
## 625 45152.29 2 56215.86
## 626 57409.57 2 45152.29
## 627 35151.43 2 57409.57
## 628 34991.43 2 35151.43
## 629 45944.71 2 34991.43
## 630 57944.71 2 45944.71
## 631 55706.29 2 57944.71
## 632 88593.71 2 55706.29
## 633 77359.43 2 88593.71
## 634 79878.71 2 77359.43
## 635 81753.00 2 79878.71
## 636 75716.00 2 81753.00
## 637 67381.43 2 75716.00
## 638 63528.57 2 67381.43
## 639 49682.86 2 63528.57
## 640 47815.00 2 49682.86
## 641 46546.14 2 47815.00
## 642 44808.71 2 46546.14
## 643 42959.57 2 44808.71
## 644 46023.86 2 42959.57
## 645 51309.57 2 46023.86
## 646 68447.29 2 51309.57
## 647 84959.29 2 68447.29
## 648 81666.29 2 84959.29
## 649 82700.86 2 81666.29
## 650 89422.14 2 82700.86
## 651 104812.71 2 89422.14
## 652 98812.71 2 104812.71
## 653 64779.86 2 98812.71
## 654 61862.86 2 64779.86
## 655 58376.43 2 61862.86
## 656 59503.57 2 58376.43
## 657 55429.43 2 59503.57
## 658 44454.57 2 55429.43
## 659 47184.00 2 44454.57
## 660 52126.71 2 47184.00
## 661 51202.00 2 52126.71
## 662 64437.14 2 51202.00
## 663 64297.14 2 64437.14
## 664 64628.57 2 64297.14
## 665 51413.14 2 64628.57
## 666 52969.43 2 51413.14
## 667 54135.29 2 52969.43
## 668 48799.43 2 54135.29
## 669 41907.86 2 48799.43
## 670 45382.00 2 41907.86
## 671 42633.29 2 45382.00
## 672 46624.71 2 42633.29
## 673 44051.86 2 46624.71
## 674 35852.86 2 44051.86
## 675 29737.71 2 35852.86
## 676 29734.86 2 29737.71
## 677 32881.71 2 29734.86
## 678 38298.57 2 32881.71
## 679 40886.14 2 38298.57
## 680 38601.86 2 40886.14
## 681 38628.86 2 38601.86
## 682 39142.57 2 38628.86
## 683 32666.14 2 39142.57
## 684 39911.57 2 32666.14
## 685 39336.29 2 39911.57
## 686 39678.86 2 39336.29
## 687 41963.14 2 39678.86
## 688 54220.57 2 41963.14
## 689 63901.86 2 54220.57
## 690 73116.00 2 63901.86
## 691 60863.86 2 73116.00
## 692 56293.86 2 60863.86
## 693 52725.00 2 56293.86
## 694 58625.00 2 52725.00
## 695 47513.00 2 58625.00
## 696 40300.14 2 47513.00
## 697 33312.43 2 40300.14
## 698 29556.71 2 33312.43
## 699 27816.71 2 29556.71
## 700 34120.29 2 27816.71
## 701 32132.57 2 34120.29
## 702 32902.57 2 32132.57
## 703 39694.14 2 32902.57
## 704 72501.29 2 39694.14
## 705 79551.14 2 72501.29
## 706 99637.71 2 79551.14
## 707 95424.29 2 99637.71
## 708 98395.14 2 95424.29
## 709 115594.71 2 98395.14
## 710 114267.57 2 115594.71
## 711 88353.29 2 114267.57
## 712 88750.86 2 88353.29
## 713 78835.71 2 88750.86
## 714 75519.14 2 78835.71
## 715 73202.86 2 75519.14
## 716 53433.29 2 73202.86
## 717 48165.71 2 53433.29
## 718 52163.14 2 48165.71
## 719 49306.86 2 52163.14
## 720 36846.86 2 49306.86
## 721 43220.57 2 36846.86
## 722 38952.29 2 43220.57
## 723 41522.29 2 38952.29
## 724 39090.00 2 41522.29
## 725 28452.57 2 39090.00
## 726 32975.00 2 28452.57
## 727 33690.71 2 32975.00
## 728 26405.29 2 33690.71
## 729 47087.43 2 26405.29
## 730 49660.29 2 47087.43
## 731 47409.71 2 49660.29
## 732 53881.71 2 47409.71
## 733 45189.57 2 53881.71
## 734 45503.86 2 45189.57
## 735 54640.14 2 45503.86
## 736 39131.29 2 54640.14
## 737 35024.14 2 39131.29
## 738 44755.43 2 35024.14
## 739 41063.29 2 44755.43
## 740 42783.29 2 41063.29
## 741 45952.57 2 42783.29
## 742 44937.43 2 45952.57
## 743 40838.43 2 44937.43
## 744 48838.43 2 40838.43
## 745 43139.14 2 48838.43
## 746 67134.29 2 43139.14
## 747 73224.29 2 67134.29
## 748 68770.71 2 73224.29
## 749 59539.29 2 68770.71
## 750 82179.86 2 59539.29
## 751 74252.14 2 82179.86
## 752 73015.00 2 74252.14
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 595 51916.41 16138.969
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 1969.993223 4019.420853 -517.239993 2456.130940 -2928.437923
## 7 8 9 10 11
## 533.718859 -5633.833865 -1212.553138 -3993.458050 -471.314121
## 12 13 14 15 16
## -4985.438419 -1687.153089 -976.978284 306.788648 -3296.906349
## 17 18 19 20 21
## -448.366493 -2190.405106 6537.715918 -1526.403551 -1214.494667
## 22 23 24 25 26
## 1464.305987 -1179.421540 235.225544 1701.983176 -7076.965238
## 27 28 29 30 31
## 913.297599 8175.162882 477.864941 46.326028 -2343.392144
## 32 33 34 35 36
## 1610.235651 4620.330858 1212.449998 2480.458498 -1765.121530
## 37 38 39 40 41
## 4686.473896 4350.083927 -2197.698630 -2932.324281 -1092.331274
## 42 43 44 45 46
## -10734.989758 7203.493873 2544.929051 1378.318008 8127.259441
## 47 48 49 50 51
## 775.179037 6612.821639 6844.100346 -5711.149963 -4695.753003
## 52 53 54 55 56
## -5013.228132 -7930.806381 6060.437330 -4084.438272 -4935.660460
## 57 58 59 60 61
## 3778.191494 853.332613 -54.318196 122.905587 -5011.739707
## 62 63 64 65 66
## 18071.039210 3747.344938 -3521.339914 6003.648070 7463.590250
## 67 68 69 70 71
## 14806.663107 1965.557851 -12959.692819 -1197.431482 4727.861330
## 72 73 74 75 76
## -4786.388413 -4346.376023 -10483.724390 2389.866234 -5445.364704
## 77 78 79 80 81
## 978.432742 -6931.208955 432.176947 -2449.641258 -2796.472654
## 82 83 84 85 86
## -4043.956056 -668.416186 2196.170862 3679.343622 436.499295
## 87 88 89 90 91
## -515.447082 165.829826 4277.065094 -1147.758128 1154.642321
## 92 93 94 95 96
## -2050.984546 -1049.708805 164.342078 265.105051 -7489.776966
## 97 98 99 100 101
## 2323.521108 -8641.314051 -3047.066571 -4156.864433 -1872.958899
## 102 103 104 105 106
## -1394.454656 3054.560643 -2424.960199 2502.157433 -1216.192026
## 107 108 109 110 111
## 910.567074 2543.430726 -3170.152792 -4763.195821 -924.993193
## 112 113 114 115 116
## 1831.473207 11647.207438 -1183.344933 2710.159074 4322.330797
## 117 118 119 120 121
## 3591.345823 -992.222094 -4631.064410 -3689.165795 2319.154277
## 122 123 124 125 126
## -1712.987703 1343.375572 8872.698709 935.478884 215.351455
## 127 128 129 130 131
## -2445.488879 2700.338406 7115.117843 1127.540574 -8389.758387
## 132 133 134 135 136
## 1773.235717 4171.821630 -3096.646598 -1387.361676 -837.307695
## 137 138 139 140 141
## -3872.244877 1157.324828 -507.447050 -2927.824344 1681.434803
## 142 143 144 145 146
## -1898.140857 -7859.755662 1946.780837 -3542.954067 2017.809119
## 147 148 149 150 151
## -313.018370 972.668976 -394.257839 1318.958638 1169.435753
## 152 153 154 155 156
## 3351.991843 -4836.921491 -1193.641913 -3262.140451 5906.629211
## 157 158 159 160 161
## 9753.842598 -3573.762473 -4936.216227 3420.781294 60.407254
## 162 163 164 165 166
## 2575.445304 -5998.442704 -6879.245915 3979.618308 17267.254967
## 167 168 169 170 171
## 3651.529897 -355.106268 -2417.915324 -1106.061127 3573.268628
## 172 173 174 175 176
## -220.890570 -8077.646266 2789.481687 4276.967683 611.168694
## 177 178 179 180 181
## 8735.891640 -9197.803449 -3509.211181 -10812.454182 -11393.195004
## 182 183 184 185 186
## 1003.438530 9094.075084 -1536.546613 5816.578879 6496.550143
## 187 188 189 190 191
## 13149.484002 8515.918997 -3934.827678 2532.271866 10434.239336
## 192 193 194 195 196
## -1518.480104 -2360.965448 -10238.994449 -6418.435339 1125.385856
## 197 198 199 200 201
## -5328.294499 -9929.240290 5182.448306 -3210.665516 -1869.260134
## 202 203 204 205 206
## -963.080264 6340.387403 9786.352585 557.223571 2898.542820
## 207 208 209 210 211
## 3085.482158 5785.209415 12866.466870 -5573.507248 -11251.504745
## 212 213 214 215 216
## -5723.719219 -10690.245756 -5254.332796 1323.450533 -13184.897981
## 217 218 219 220 221
## 16130.506513 7697.853899 1478.752522 26645.526470 12675.185445
## 222 223 224 225 226
## 7544.231176 14249.955858 -3628.286687 -1534.949130 3931.270392
## 227 228 229 230 231
## 510.525031 2869.001220 9122.144905 5991.663668 -1729.557653
## 232 233 234 235 236
## -1697.525176 9516.097288 -11368.625197 -7258.760204 -8584.896366
## 237 238 239 240 241
## -10214.127950 2893.503662 1205.546170 -8423.963323 -9171.766988
## 242 243 244 245 246
## 8859.432884 -7911.453379 2292.504910 -10461.670048 -4280.176430
## 247 248 249 250 251
## 1186.001575 797.876263 -12497.008587 3384.178239 1853.878984
## 252 253 254 255 256
## 4034.999972 2001.097238 -1272.351792 11021.982942 20847.944449
## 257 258 259 260 261
## 3304.601376 -4169.629088 4152.692361 -1641.260434 3757.615026
## 262 263 264 265 266
## -4820.555177 -10914.881438 -4838.569632 -663.804498 -5327.945521
## 267 268 269 270 271
## 8606.771610 -4380.337376 4058.171779 -2205.322631 4316.281848
## 272 273 274 275 276
## 626.264011 7221.333861 -1447.299109 11968.936255 -4566.926401
## 277 278 279 280 281
## 1691.226241 -406.675717 7802.633166 -5062.760084 -2785.995319
## 282 283 284 285 286
## -11342.081607 -2828.528479 18485.702156 7746.937557 2737.880089
## 287 288 289 290 291
## -623.845083 889.415286 6373.415125 6886.446425 -18740.510980
## 292 293 294 295 296
## -11247.272197 -8298.673728 9448.774918 2936.034918 -1289.409527
## 297 298 299 300 301
## 27286.403076 10124.830076 4999.951796 9618.609100 2987.638946
## 302 303 304 305 306
## -916.971930 7974.337849 -24193.699551 -3609.933728 -272.986060
## 307 308 309 310 311
## -7064.360226 -4107.683649 2781.338732 -9313.648264 -3399.134923
## 312 313 314 315 316
## -8358.884071 1360.439651 -3325.465560 1871.451839 -4231.075641
## 317 318 319 320 321
## 27283.866179 -729.957833 3267.037389 10814.514577 5624.857071
## 322 323 324 325 326
## 32429.807661 5331.871837 -20729.127220 1836.077204 1146.436331
## 327 328 329 330 331
## -6439.556263 -1761.633914 -33312.204329 676.672393 -2472.897893
## 332 333 334 335 336
## -252.557248 -3305.140025 3949.511106 -531.720687 -7037.448026
## 337 338 339 340 341
## -3229.209901 -2306.050598 -7790.091716 3714.009797 -1468.880273
## 342 343 344 345 346
## -1830.162900 -1083.599084 93.296210 409.949688 -1679.121701
## 347 348 349 350 351
## -9509.698370 -13318.403455 2142.985742 -4455.074200 -3796.121637
## 352 353 354 355 356
## -6118.616288 1598.581800 1261.581751 2652.448623 -3842.171906
## 357 358 359 360 361
## -605.550106 594.057393 6941.497204 246.330927 -68.203327
## 362 363 364 365 366
## 2551.748380 -2769.030656 -912.058490 -8781.274662 -4704.874078
## 367 368 369 370 371
## -6304.528636 -5060.426248 -7372.928254 4874.941181 273.798483
## 372 373 374 375 376
## 7035.421615 -7676.715508 -2338.080762 -3464.892858 -2550.853227
## 377 378 379 380 381
## -12542.322670 1767.570771 -10741.307221 5550.620676 9237.669348
## 382 383 384 385 386
## 3083.633693 -2424.754962 1565.588503 6713.257235 11410.339817
## 387 388 389 390 391
## -5757.099653 -5364.990212 -195.935656 8520.661491 1809.804289
## 392 393 394 395 396
## 11214.932490 -9843.443813 2741.899883 686.212774 531.100118
## 397 398 399 400 401
## -689.769209 -608.646076 -14540.171994 8404.988363 -1244.040804
## 402 403 404 405 406
## -1437.309563 6914.720884 -7963.400464 -1364.712172 -2597.926277
## 407 408 409 410 411
## -5888.938041 -2945.470033 -4003.621771 -8846.585096 6016.211458
## 412 413 414 415 416
## 1567.559497 -7430.222974 -7775.565404 14114.653275 3784.456971
## 417 418 419 420 421
## 4474.007593 -8039.415164 -4788.469555 -2662.101627 2755.172659
## 422 423 424 425 426
## -14055.107008 -2893.221737 -9197.404766 2888.914428 6884.969355
## 427 428 429 430 431
## 6525.739052 -4004.399790 -4156.001426 -4772.274337 -1854.389473
## 432 433 434 435 436
## -5775.772880 -6707.440888 -6048.589137 -1504.366815 -948.487182
## 437 438 439 440 441
## -5064.753971 2482.571474 4766.186722 -5098.819874 -2218.299030
## 442 443 444 445 446
## 1514.653273 -3883.490321 2777.698380 -6615.573500 -12173.327209
## 447 448 449 450 451
## -4620.806754 9532.080559 -2082.610010 4702.285208 -5891.478671
## 452 453 454 455 456
## -1168.360419 340.411927 2991.713029 -12283.390276 3298.567336
## 457 458 459 460 461
## -6744.557901 6454.162606 2985.981338 2499.036522 -3840.888078
## 462 463 464 465 466
## 2078.540109 -9.524351 1790.836686 -514.922572 3352.753259
## 467 468 469 470 471
## -2621.906804 5807.271562 -6912.434913 -2970.716840 -2222.670342
## 472 473 474 475 476
## -4686.535783 2957.385948 7780.560688 -5992.632574 1477.890885
## 477 478 479 480 481
## -6175.050775 -2868.583570 1980.324076 -12944.565461 -9828.980516
## 482 483 484 485 486
## -1311.041004 -75.657695 -1041.273438 -1411.527711 -9648.311349
## 487 488 489 490 491
## 10995.703776 6207.799920 7429.481455 -5390.896307 5380.783910
## 492 493 494 495 496
## 9332.157254 6133.514608 -13375.786586 -10547.703168 -3475.118075
## 497 498 499 500 501
## -1148.656234 -563.105983 -7658.399410 549.340818 4242.100126
## 502 503 504 505 506
## 5494.706759 678.923565 102.493889 -7217.029406 555.842717
## 507 508 509 510 511
## -5053.968633 1806.080497 -1306.186698 -8169.163745 -649.155795
## 512 513 514 515 516
## -2713.892013 -631.079556 1295.973208 -9515.806832 -7827.389219
## 517 518 519 520 521
## 24197.574195 9874.793175 5972.693882 -5225.472824 2866.621058
## 522 523 524 525 526
## 17092.447026 11621.412963 -23966.394301 -5024.912563 -3727.490449
## 527 528 529 530 531
## 4560.083011 -342.849853 -11091.541390 4347.116361 13895.822272
## 532 533 534 535 536
## -4918.012506 4399.142800 5598.607605 -1725.790801 -4494.831604
## 537 538 539 540 541
## -7056.626199 -2119.126684 8298.621120 150.469297 -8119.442646
## 542 543 544 545 546
## 1796.082338 -605.050064 360.937520 -11031.221565 -11114.506612
## 547 548 549 550 551
## 1943.339629 6934.101314 -1338.523823 815.455669 -7731.796202
## 552 553 554 555 556
## 8519.850116 914.295151 -11930.708180 9116.177937 8666.177402
## 557 558 559 560 561
## 150.414677 4898.065167 -3511.339891 14144.715657 21599.662029
## 562 563 564 565 566
## -6275.375332 -9551.832813 6835.905758 311.168717 3527.537609
## 567 568 569 570 571
## -7298.350344 -17276.612510 6606.783589 6424.168669 1928.685236
## 572 573 574 575 576
## 3133.057933 1817.799935 -2112.717639 14753.346922 -9542.903198
## 577 578 579 580 581
## -6202.143229 8717.714267 2909.516595 -6486.011181 7528.582504
## 582 583 584 585 586
## -3743.177007 -2744.294765 15717.861536 -14405.187650 8436.323393
## 587 588 589 590 591
## 122.306508 -6165.209701 -742.039084 262.251781 -10639.328467
## 592 593 594 595 596
## 1759.004985 -7162.893212 3020.416944 8846.505388 -7468.285717
## 597 598 599 600 601
## 5844.743555 2762.997842 6897.343207 -3116.259781 6199.937111
## 602 603 604 605 606
## -8220.262676 2283.711313 1310.158737 3183.432692 1554.184054
## 607 608 609 610 611
## 459.838665 -5750.685024 8100.822119 -1114.920041 -2516.317760
## 612 613 614 615 616
## -3411.612896 -8204.497190 11942.607011 4991.078176 -9236.381215
## 617 618 619 620 621
## 11650.389032 6137.260502 -5460.755283 26433.015886 -12615.465552
## 622 623 624 625 626
## -6659.648826 3231.479622 -4071.834180 -10532.584061 11298.100733
## 627 628 629 630 631
## -21566.371964 -2466.218430 8625.516597 11147.548650 -1474.578323
## 632 633 634 635 636
## 33349.780846 -6342.266492 5898.138773 5592.465901 -2066.368897
## 637 638 639 640 641
## -5177.074722 -1817.958863 -12329.764269 -2216.811304 -1869.396344
## 642 643 644 645 646
## -2508.872425 -2854.604092 1809.760100 4443.922829 17007.865200
## 647 648 649 650 651
## 18690.460497 1109.491497 4993.522828 10819.585402 20394.173186
## 652 653 654 655 656
## 1076.585524 -27764.422418 -1232.512058 -2194.836610 1949.141479
## 657 658 659 660 661
## -3100.327335 -10549.795255 1676.267198 4257.184559 -944.500929
## 662 663 664 665 666
## 13090.804782 1498.327138 1950.898857 -11551.317575 1440.386851
## 667 668 669 670 671
## 1259.577203 -5085.105686 -7359.516174 2077.959952 -3676.958636
## 672 673 674 675 676
## 2692.954947 -3333.718069 -9306.403686 -8326.884610 -3038.258494
## 677 678 679 680 681
## 111.070958 2804.926811 705.247335 -3818.085155 -1814.473995
## 682 683 684 685 686
## -1324.123031 -8245.072786 4604.462536 -2240.351935 -1399.981060
## 687 688 689 690 691
## 587.874788 10868.692199 9943.524458 10780.371399 -9444.844830
## 692 693 694 695 696
## -3412.965146 -3027.363815 5960.797536 -10256.520845 -7854.073268
## 697 698 699 700 701
## -8600.443121 -6309.630935 -4799.780567 3009.427130 -4432.819203
## 702 703 704 705 706
## -1942.833732 4182.450383 31112.790791 9774.358197 23760.630430
## 707 708 709 710 711
## 2166.126889 8782.898325 23411.762706 7201.689653 -17564.208464
## 712 713 714 715 716
## 5257.206894 -5001.957781 261.125211 814.695950 -16950.574456
## 717 718 719 720 721
## -5111.373615 3444.127709 -2871.165728 -12859.598278 4295.856182
## 722 723 724 725 726
## -5487.656769 775.725860 -3880.401929 -12413.153727 1313.929016
## 727 728 729 730 731
## -1883.651226 -9788.393237 17197.890704 1874.320036 -2602.565776
## 732 733 734 735 736
## 5816.872136 -8475.545392 -639.877187 8224.454522 -15190.105568
## 737 738 739 740 741
## -5877.307165 7407.922943 -4704.781241 210.060249 1891.015859
## 742 743 744 745 746
## -1866.535915 -5087.124476 6459.773836 -6161.977479 22764.804041
## 747 748 749 750 751
## 8091.610164 -1631.688204 -7009.404898 23619.197372 -3899.589026
## 752
## 1723.184296
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17299.29 20119.58 24333.38 24054.01 26385.15 23743.00 24452.55 19729.70
## 10 11 12 13 14 15 16 17
## 19468.74 16836.60 17606.72 14367.01 14417.69 15076.07 16756.62 15092.51
## 18 19 20 21 22 23 24 25
## 16117.41 15496.86 22512.40 21605.07 21089.84 22961.99 22294.35 22940.73
## 26 27 28 29 30 31 32 33
## 24769.25 18754.99 20464.84 28228.14 28285.25 27961.25 25613.05 27002.24
## 34 35 36 37 38 39 40 41
## 30808.98 31154.11 32549.98 30084.10 34092.92 37270.70 34354.61 31195.62
## 42 43 44 45 46 47 48 49
## 30054.28 20722.79 28170.50 30583.97 31662.88 38436.39 37935.75 42553.90
## 50 51 52 53 54 55 56 57
## 46750.15 39517.04 34136.80 29206.52 22415.71 28646.30 25259.23 21591.81
## 58 59 60 61 62 63 64 65
## 25958.52 27206.18 27500.38 27908.31 23818.25 40252.80 42079.34 37370.21
## 66 67 68 69 70 71 72 73
## 41537.41 46406.62 56974.01 55006.55 40389.15 37918.57 40907.96 35261.95
## 74 75 76 77 78 79 80 81
## 30757.15 21548.42 24719.65 20683.85 22750.21 17693.97 19690.36 18924.19
## 82 83 84 85 86 87 88 89
## 17961.10 16048.27 17313.97 20887.94 25263.93 26244.45 26269.17 26880.08
## 90 91 92 93 94 95 96 97
## 30966.19 29807.79 30797.70 28880.42 28087.80 28452.47 28855.21 22493.34
## 98 99 100 101 102 103 104 105
## 25479.89 18576.21 17443.15 15502.39 15799.31 16470.30 20900.67 19992.84
## 106 107 108 109 110 111 112 113
## 23470.76 23262.72 24923.00 27772.58 25294.34 21771.42 22044.24 24665.51
## 114 115 116 117 118 119 120 121
## 35427.34 33637.27 35457.38 38427.37 40364.79 38075.06 32945.02 29320.99
## 122 123 124 125 126 127 128 129
## 31384.13 29680.34 30850.73 38378.66 38024.51 37094.92 33988.09 35752.45
## 130 131 132 133 134 135 136 137
## 41099.32 40544.90 31829.76 33082.61 36242.22 32686.79 31089.31 30182.96
## 138 139 140 141 142 143 144 145
## 26772.53 28173.59 27945.40 25653.57 27658.86 26296.61 19959.22 22961.10
## 146 147 148 149 150 151 152 153
## 20808.33 23757.30 24292.19 25867.54 26047.90 27686.42 28974.87 31978.35
## 154 155 156 157 158 159 160 161
## 27491.36 26761.28 24339.66 30178.01 41594.19 39940.22 37330.08 42302.88
## 162 163 164 165 166 167 168 169
## 43698.13 47081.73 42590.53 37942.10 43316.03 59464.04 61655.25 60084.34
## 170 171 172 173 174 175 176 177
## 56940.06 55354.45 58031.46 57064.79 49429.80 52226.60 55933.83 55969.68
## 178 179 180 181 182 183 184 185
## 63031.09 53623.21 50404.88 41300.48 32919.85 36394.92 46402.83 45863.99
## 186 187 188 189 190 191 192 193
## 51760.45 57451.09 68132.08 73364.97 67119.30 67310.90 74314.34 70031.68
## 194 195 196 197 198 199 200 201
## 65596.85 54942.44 49029.04 50439.87 46076.24 38319.12 44683.09 42927.26
## 202 203 204 205 206 207 208 209
## 42568.65 43042.47 49772.22 58577.35 58210.46 59918.95 61559.08 65314.39
## 210 211 212 213 214 215 216 217
## 74691.36 66849.08 55149.86 49809.67 40891.19 37877.69 40961.90 31076.49
## 218 219 220 221 222 223 224 225
## 47889.43 55140.96 56034.33 78584.39 86008.48 87992.76 95512.29 86548.81
## 226 227 228 229 230 231 232 233
## 80604.02 80189.90 76871.57 76041.00 80733.19 82084.56 76572.67 71830.90
## 234 235 236 237 238 239 240 241
## 77431.05 64205.19 56317.04 48343.84 40034.78 44187.03 46319.39 39832.05
## 242 243 244 245 246 247 248 249
## 33571.42 43756.60 38057.92 41956.38 34293.46 33011.57 36632.27 39429.44
## 250 251 252 253 254 255 256 257
## 30345.68 36227.55 39993.00 45138.62 47831.21 47328.59 57532.06 74863.68
## 258 259 260 261 262 263 264 265
## 74680.49 68054.45 69522.26 65778.81 67211.27 61028.02 50404.14 46469.09
## 266 267 268 269 270 271 272 273
## 46676.52 42820.09 51540.91 47849.26 51956.75 50091.15 54120.02 54413.24
## 274 275 276 277 278 279 280 281
## 60373.73 58030.35 67611.78 61594.06 61802.10 60166.80 65855.33 59645.14
## 282 283 284 285 286 287 288 289
## 56241.51 45892.67 44304.58 61373.78 66851.55 67257.13 64699.16 63795.16
## 290 291 292 293 294 295 296 297
## 67758.27 71631.51 52807.84 43003.53 37071.23 47294.97 50506.12 49628.45
## 298 299 300 301 302 303 304 305
## 73595.88 79485.05 80146.39 84715.22 82930.83 78008.09 81442.13 56578.36
## 306 307 308 309 310 311 312 313
## 52874.84 52557.65 46406.54 43642.38 47211.65 39834.28 38568.46 33181.42
## 314 315 316 317 318 319 320 321
## 36930.18 36119.26 39914.50 37917.99 63460.53 61322.11 62930.34 70852.86
## 322 323 324 325 326 327 328 329
## 73217.62 98458.41 96851.41 72910.07 71719.28 70092.13 62119.92 59269.35
## 330 331 332 333 334 335 336 337
## 29501.76 33154.47 33589.84 35887.85 35234.92 40947.43 42012.88 37305.35
## 338 339 340 341 342 343 344 345
## 36527.19 36652.66 32015.85 37958.17 38615.31 38871.31 39738.85 41507.91
## 346 347 348 349 350 351 352 353
## 43312.69 43066.70 36077.97 26734.87 32029.07 30900.84 30494.76 28133.70
## 354 355 356 357 358 359 360 361
## 32768.42 36487.27 40908.74 39114.84 40363.23 42481.50 49806.95 50352.35
## 362 363 364 365 366 367 368 369
## 50552.11 52992.03 50499.20 49948.99 42663.59 39886.81 36099.85 33899.50
## 370 371 372 373 374 375 376 377
## 29994.49 37213.63 39479.01 47290.14 41318.65 40771.04 39322.14 38859.32
## 378 379 380 381 382 383 384 385
## 29813.14 34367.88 27485.09 35626.90 45862.51 49394.33 47683.98 49656.89
## 386 387 388 389 390 391 392 393
## 55818.37 65214.39 58489.70 53010.08 52741.34 60051.34 60569.78 69156.73
## 394 395 396 397 398 399 400 401
## 58365.10 59917.22 59481.47 58970.20 57471.36 56244.60 43128.01 51632.76
## 402 403 404 405 406 407 408 409
## 50642.60 49618.56 55959.54 48572.28 47889.93 46232.37 41950.33 40792.05
## 410 411 412 413 414 415 416 417
## 38874.16 33023.93 40822.58 43721.37 38443.85 33578.35 48309.97 52118.56
## 418 419 420 421 422 423 424 425
## 56010.84 48550.90 44908.82 43597.26 47149.96 35678.08 35409.83 29722.66
## 426 427 428 429 430 431 432 433
## 35259.89 43509.12 50336.40 47132.29 44228.56 41182.68 41071.92 37582.87
## 434 435 436 437 438 439 440 441
## 33757.59 31017.65 32578.92 34410.90 32434.29 37254.67 43401.82 40184.73
## 442 443 444 445 446 447 448 449
## 39893.49 42871.63 40777.59 44729.57 40021.18 31137.81 29986.21 41236.32
## 450 451 452 453 454 455 456 457
## 40920.86 46518.91 42196.07 42542.45 44147.72 47830.96 37800.43 42604.13
## 458 459 460 461 462 463 464 465
## 38070.41 45568.30 49055.25 51651.17 48411.46 50730.24 50929.88 52660.49
## 466 467 468 469 470 471 472 473
## 52162.82 55078.91 52432.30 57436.01 50759.29 48392.67 46992.11 43648.19
## 474 475 476 477 478 479 480 481
## 47369.01 54762.20 49241.54 50928.77 45766.58 44160.82 46967.14 36480.84
## 482 483 484 485 486 487 488 489
## 30102.90 31954.66 34625.99 36101.96 37058.74 30759.30 43171.77 49769.38
## 490 491 492 493 494 495 496 497
## 56535.47 51296.64 56084.27 63646.20 67421.79 53807.27 44473.69 42517.23
## 498 499 500 501 502 503 504 505
## 42837.39 43621.11 38159.66 40536.04 45787.72 51415.93 52118.93 52228.46
## 506 507 508 509 510 511 512 513
## 45989.59 47316.97 43611.35 46340.90 46009.74 39784.58 40905.03 40087.94
## 514 515 516 517 518 519 520 521
## 41183.17 43798.38 36705.82 32029.57 55694.64 63778.59 67397.19 60838.52
## 522 523 524 525 526 527 528 529
## 62165.41 75623.30 82534.39 57720.20 52638.49 49363.92 53701.71 53212.68
## 530 531 532 533 534 535 536 537
## 43488.60 48433.46 60974.87 55547.29 58912.96 62863.22 59943.55 55021.05
## 538 539 540 541 542 543 544 545
## 48544.84 47213.38 55075.82 54828.59 47458.63 49661.34 49489.63 50176.94
## 546 547 548 549 550 551 552 553
## 40913.94 32826.52 37127.47 45167.67 44966.54 46656.37 40722.58 49650.70
## 554 555 556 557 558 559 560 561
## 50795.14 40670.54 50121.68 57910.44 57281.36 60845.20 56652.28 68302.05
## 562 563 564 565 566 567 568 569
## 84833.52 75017.83 63689.09 68066.69 66208.75 67384.21 59033.61 43173.50
## 570 571 572 573 574 575 576 577
## 50116.12 55965.60 57137.23 59193.20 59834.15 56987.65 69118.90 58592.43
## 578 579 580 581 582 583 584 585
## 52374.57 59904.48 61394.30 54553.42 60760.89 56378.72 53451.14 66893.33
## 586 587 588 589 590 591 592 593
## 52459.25 59734.26 58835.21 52616.61 51928.32 52201.76 43005.14 45775.61
## 594 595 596 597 598 599 600 601
## 40452.73 44658.49 53339.14 46733.26 52537.00 54892.37 60507.97 56702.35
## 602 603 604 605 606 607 608 609
## 61470.69 53118.86 54981.13 55750.14 58036.53 58605.16 58150.26 52382.61
## 610 611 612 613 614 615 616 617
## 59377.63 57456.03 54580.61 51317.78 44347.11 55748.78 59599.52 50620.47
## 618 619 620 621 622 623 624 625
## 60924.31 65069.76 58620.98 80638.75 65901.93 58303.66 60287.69 55684.87
## 626 627 628 629 630 631 632 633
## 46111.47 56717.80 37457.65 37319.20 46797.17 57180.86 55243.93 83701.70
## 634 635 636 637 638 639 640 641
## 73980.58 76160.53 77782.37 72558.50 65346.53 62012.62 50031.81 48415.54
## 642 643 644 645 646 647 648 649
## 47317.59 45814.18 44214.10 46865.65 51439.42 66268.83 80556.79 77707.33
## 650 651 652 653 654 655 656 657
## 78602.56 84418.54 97736.13 92544.28 63095.37 60571.27 57554.43 58529.76
## 658 659 660 661 662 663 664 665
## 55004.37 45507.73 47869.53 52146.50 51346.34 62798.82 62677.67 62964.46
## 666 667 668 669 670 671 672 673
## 51529.04 52875.71 53884.53 49267.37 43304.04 46310.24 43931.76 47385.58
## 674 675 676 677 678 679 680 681
## 45159.26 38064.60 32773.12 32770.64 35493.64 40180.90 42419.94 40443.33
## 682 683 684 685 686 687 688 689
## 40466.69 40911.22 35307.11 41576.64 41078.84 41375.27 43351.88 53958.33
## 690 691 692 693 694 695 696 697
## 62335.63 70308.70 59706.82 55752.36 52664.20 57769.52 48154.22 41912.87
## 698 699 700 701 702 703 704 705
## 35866.35 32616.49 31110.86 36565.39 34845.41 35511.69 41388.49 69776.78
## 706 707 708 709 710 711 712 713
## 75877.08 93258.16 89612.24 92182.95 107065.88 105917.49 83493.65 83837.67
## 714 715 716 717 718 719 720 721
## 75258.02 72388.16 70383.86 53277.09 48719.02 52178.02 49706.46 38924.72
## 722 723 724 725 726 727 728 729
## 44439.94 40746.56 42970.40 40865.73 31661.07 35574.37 36193.68 29889.54
## 730 731 732 733 734 735 736 737
## 47785.97 50012.28 48064.84 53665.12 46143.73 46415.69 54321.39 40901.45
## 738 739 740 741 742 743 744 745
## 37347.51 45768.07 42573.23 44061.56 46803.96 45925.55 42378.65 49301.12
## 746 747 748 749 750 751 752
## 44369.48 65132.68 70402.40 66548.69 58560.66 78151.73 71291.82
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8234
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 7.64741 0.4956167 3.393459
## t2* 2199.11349 23.3075449 253.558995
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 3.313389 7.711413 14.40067
## 2 lag_depvar 1831.568771 2206.192037 2663.21580Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Aug 19 00:44:18 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Aug 19 00:44:26 2024
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## =-=-=-=-= Iteration 4000 Mon Aug 19 00:44:33 2024
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## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Aug 19 00:44:48 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Aug 19 00:44:55 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Aug 19 00:45:02 2024
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Aug 19 00:45:10 2024
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## =-=-=-=-= Iteration 16000 Mon Aug 19 00:45:17 2024
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## =-=-=-=-= Iteration 18000 Mon Aug 19 00:45:24 2024
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_24 %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2024","2023","2022","2021","2020"))| Item | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|
| Agua | 4.664286 | 5.195333 | 5.410333 | 5.849167 | 6.238589 |
| Comida | 337.878571 | 366.009167 | 312.386750 | 317.896583 | 341.526250 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| Electricidad | 52.977143 | 38.104750 | 47.072333 | 29.523000 | 42.750089 |
| Enceres | 39.268286 | 18.259750 | 24.219750 | 14.801167 | 25.542679 |
| Farmacia | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 8.908268 |
| Gas/Bencina | 60.190000 | 42.636000 | 45.575000 | 13.583667 | 33.035107 |
| Diosi | 43.438429 | 55.804250 | 31.180667 | 52.687833 | 43.314250 |
| donaciones/regalos | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| Electrodomésticos/ Mantención casa | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| VTR | 21.991429 | 12.829167 | 25.156667 | 19.086917 | 19.467286 |
| Netflix | 2.385286 | 8.713833 | 7.151583 | 7.028750 | 6.971321 |
| Otros | 52.000000 | 5.481667 | 5.000000 | 0.000000 | 8.746071 |
| Total | 614.793429 | 563.738000 | 505.988083 | 474.453167 | 536.499911 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
tryCatch(uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf24 <-uf24[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf24 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf24)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 47 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2444, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2024-09-09 00:04:58 sería de: 38.557 pesos// Percentil 95% más alto proyectado: 41.724,22
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 37873.02 | 37871.98 |
| Lo.80 | 37919.40 | 37932.49 |
| Point.Forecast | 38557.45 | 40369.97 |
| Hi.80 | 40345.99 | 45170.92 |
| Hi.95 | 41325.73 | 47712.38 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(0,1,1)
##
## Coefficients:
## ma1
## -0.8110
## s.e. 0.0908
##
## sigma^2 = 30421: log likelihood = -427.76
## AIC=859.51 AICc=859.71 BIC=863.86paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(0,0,0) errors
##
## Coefficients:
## intercept xreg
## 594.1288 13.7354
## s.e. 191.3911 5.9602
##
## sigma^2 = 29498: log likelihood = -432.27
## AIC=870.55 AICc=870.93 BIC=877.12forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 825.1301 | 724.0723 | 784.4050 |
| Lo.80 | 941.6475 | 842.3990 | 874.5554 |
| Point.Forecast | 1161.7540 | 1065.9233 | 1074.0604 |
| Hi.80 | 1381.8605 | 1329.7501 | 1319.0361 |
| Hi.95 | 1498.3778 | 1469.4116 | 1470.5699 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [4] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.9
## [7] tidytext_0.4.1 DT_0.32 janitor_2.2.0
## [10] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [13] xts_0.13.2 forecast_8.21.1 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-11
## [19] NLP_0.2-1 tsibble_1.1.4 lubridate_1.9.3
## [22] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.2
## [25] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
## [28] gsynth_1.2.1 lattice_0.20-45 GGally_2.2.1
## [31] ggplot2_3.5.0 gridExtra_2.3 plotrix_3.8-4
## [34] sparklyr_1.8.4 httr_1.4.7 readxl_1.4.3
## [37] zoo_1.8-12 stringr_1.5.1 stringi_1.8.3
## [40] data.table_1.15.0 reshape2_1.4.4 fUnitRoots_4021.80
## [43] plyr_1.8.9 readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] uuid_1.2-0 systemfonts_1.0.5 selectr_0.4-2
## [4] lazyeval_0.2.2 websocket_1.4.1 crosstalk_1.2.1
## [7] listenv_0.9.1 digest_0.6.34 foreach_1.5.2
## [10] htmltools_0.5.7 fansi_1.0.6 ggfortify_0.4.16
## [13] magrittr_2.0.3 doParallel_1.0.17 tzdb_0.4.0
## [16] globals_0.16.2 vroom_1.6.5 sandwich_3.1-0
## [19] askpass_1.2.0 timechange_0.3.0 anytime_0.3.9
## [22] tseries_0.10-55 colorspace_2.1-0 xfun_0.42
## [25] crayon_1.5.2 jsonlite_1.8.8 iterators_1.0.14
## [28] glue_1.7.0 gtable_0.3.4 car_3.1-2
## [31] quantmod_0.4.26 abind_1.4-5 mvtnorm_1.2-4
## [34] DBI_1.2.2 rngtools_1.5.2 Rcpp_1.0.12
## [37] lfe_2.9-0 viridisLite_0.4.2 xtable_1.8-4
## [40] bit_4.0.5 Formula_1.2-5 htmlwidgets_1.6.4
## [43] timeSeries_4032.109 gplots_3.1.3.1 ellipsis_0.3.2
## [46] spatial_7.3-14 farver_2.1.1 pkgconfig_2.0.3
## [49] nnet_7.3-16 sass_0.4.8 dbplyr_2.4.0
## [52] chromote_0.2.0 utf8_1.2.4 labeling_0.4.3
## [55] tidyselect_1.2.0 rlang_1.1.3 later_1.3.2
## [58] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [61] cachem_1.0.8 cli_3.6.2 generics_0.1.3
## [64] evaluate_0.23 fastmap_1.1.1 yaml_2.3.8
## [67] processx_3.8.3 knitr_1.45 bit64_4.0.5
## [70] caTools_1.18.2 future_1.33.1 nlme_3.1-153
## [73] doRNG_1.8.6 slam_0.1-50 xml2_1.3.6
## [76] tokenizers_0.3.0 compiler_4.1.2 rstudioapi_0.15.0
## [79] curl_5.2.0 bslib_0.6.1 highr_0.10
## [82] ps_1.7.6 fBasics_4032.96 Matrix_1.6-5
## [85] its.analysis_1.6.0 urca_1.3-3 vctrs_0.6.5
## [88] pillar_1.9.0 lifecycle_1.0.4 lmtest_0.9-40
## [91] jquerylib_0.1.4 bitops_1.0-7 R6_2.5.1
## [94] promises_1.2.1 KernSmooth_2.23-20 janeaustenr_1.0.0
## [97] parallelly_1.37.0 codetools_0.2-18 ggstats_0.5.1
## [100] assertthat_0.2.1 boot_1.3-28 gtools_3.9.5
## [103] MASS_7.3-54 openssl_2.1.1 withr_3.0.0
## [106] fracdiff_1.5-3 parallel_4.1.2 hms_1.1.3
## [109] quadprog_1.5-8 timeDate_4032.109 rmarkdown_2.25
## [112] snakecase_0.11.1 carData_3.0-5 TTR_0.24.4#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))